Single nucleotide polymorphism (SNP) and genetic copy number (CN) have been used extensively for genetic analysis. Fast and reliable hybridization-based SNP/CN assays have been developed. (See, Wang et al., Science, 280:1077-1082, 1998; Gingeras, et al., Genome Research, 8:435-448, 1998; Halushka, et al., Nature Genetics, 22:239-247, 1999; Korbel et al., “Systematic prediction and validation of breakpoints associated with copy-number variants in the human genome,” PNAS USA, 104(24):10110-10115, 2007; and Nigel P. Carter, “Methods and strategies for analyzing copy number variation using DNA microarrays,” Nature Genetics, 30:S16-S21, 2007, incorporated herein by reference in their entireties). Computer-implemented methods for discovering polymorphism and determining genotypes are disclosed in, for example, U.S. Pat. No. 5,858,659 (incorporated herein by reference in its entirety for all purposes). However, there is still need for additional methods for determining genotypes and displaying the large amount of genetic information obtained from such experiments in a user-friendly interactive computer application.
Data can be statistically manipulated to eliminate independent variables by use of covariate adjusters. Sample data obtained from microarray experiments is in the form of intensity values. Intensity values correspond to the hybridization of labeled genetic material to a probe mounted on a microarray. Such intensity values can have many intrinsic independent variables that are unrelated to the variable being studied, and which can confound the result, i.e. mask the result to yield unpredictable results. Strides have been made in the past to remove these independent variables so that genetic sample analyses on microarrays may be more consistent and of a higher quality, i.e. more reliable and determinative when studying disease. However, many variables still plague data and sample analyses. Likewise, display of such intensity measurements in genomic microarray studies can be influenced by many independent variables. Much can be done to eliminate these variables to reveal underlying patterns and signals impacting data interpretation. Various image data filters and data manipulations strategies may be employed to remove these independent variables to again improve consistency and quality of data presentation and analysis. The discovery of new ways of increasing the quality of these data and of the scanning, measuring and displaying of these intensities are in dire need to keep pace with the rapid advancement of the application of diagnostic utilities associated with microarray-based genetic tests.